News from the AI & ML world
DeeperML
@pub.towardsai.net
//
Towards AI has announced the release of Lesson 6 in their popular 10-Hour LLM Primer course. This new lesson focuses on advanced techniques for gaining "real control" over Large Language Models (LLMs), moving beyond basic prompting and retrieval. It aims to equip professionals with the knowledge to effectively fine-tune open models, even with limited datasets of just a few hundred examples. The lesson promises to guide users on when to undertake fine-tuning, how to do it efficiently, and critically, how to determine if the fine-tuning process has been successful.
The curriculum delves into crucial fine-tuning methods such as LoRA (Low-Rank Adaptation) and RLHF (Reinforcement Learning from Human Feedback), along with other related techniques like QLoRA and reinforcement learning with methods like PPO, DPO, and GRPO. A significant portion of the lesson is dedicated to understanding and avoiding common pitfalls like overfitting, underfitting, and hallucinations, ensuring more robust and reliable LLM behavior. Additionally, the course includes a practical walkthrough of training using Unsloth, a framework that enables efficient training even on free GPU resources.
This expanded lesson is part of the broader 10-Hour LLM Primer, which is designed for software professionals but accessible to anyone interested in understanding LLMs. The course covers essential skills for production-ready AI applications, including model evaluation, agent workflows, tool integration, and optimization principles like quantization and prompt injection mitigation. Towards AI highlights that this comprehensive approach empowers users to go beyond basic LLM interaction and develop customized, efficient, and safe AI solutions.
References :
The curriculum delves into crucial fine-tuning methods such as LoRA (Low-Rank Adaptation) and RLHF (Reinforcement Learning from Human Feedback), along with other related techniques like QLoRA and reinforcement learning with methods like PPO, DPO, and GRPO. A significant portion of the lesson is dedicated to understanding and avoiding common pitfalls like overfitting, underfitting, and hallucinations, ensuring more robust and reliable LLM behavior. Additionally, the course includes a practical walkthrough of training using Unsloth, a framework that enables efficient training even on free GPU resources.
This expanded lesson is part of the broader 10-Hour LLM Primer, which is designed for software professionals but accessible to anyone interested in understanding LLMs. The course covers essential skills for production-ready AI applications, including model evaluation, agent workflows, tool integration, and optimization principles like quantization and prompt injection mitigation. Towards AI highlights that this comprehensive approach empowers users to go beyond basic LLM interaction and develop customized, efficient, and safe AI solutions.
Share:
References :
- academy.towardsai.net: This course is initially designed as a 1-day Bootcamp for Software Professionals (language agnostic).
- pub.towardsai.net: If you’ve watched the first two tutorials in the 10-hour LLM Primer, you already know what prompting can do, and you’ve seen how retrieval takes it a step further.
- towardsdatascience.com: How to Fine-Tune Small Language Models to Think with Reinforcement Learning
- Towards AI: Lesson 6 is Live: Fine-Tuning, LoRA, RLHF & the Tools That Give You Real Control
Classification:
- HashTags: #LLMs #FineTuning #RLHF
- Company: Towards AI
- Target: AI developers, Researchers
- Product: LLM Primer
- Feature: Fine-Tuning
- Type: AI
- Severity: Informative